Fish and health with national fish promotion programme
This is an open assessment looking at potential health effects of a national fish promotion program in Finland. The details of the assessment are described at Opasnet. This file contains the R code to run the assessment model.
Knit to html for best performance.
Calculation is based on BONUS GOHERR project and its http://en.opasnet.org/w/Goherr_assessment.
What needs to be done for PFAS assessment?
- Amount should be gender and age-specific, because we need to worry about young mothers. Solution: take KKE amounts of fish, and scale those with the Goherr subgroup-specific proportions. DONE
- Amounts should reflect the actual fish consumption in Porvoo. Solution: postpone and use national statistics. DONE
- Infant’s dioxin concentration module must be added. Solution: Use Goherr model for expo_indir DONE
- The module must be updated to match PFAS as well. Solution: update the module to contain column Exposure_agent. Change body fat parameter to volume of distribution.
- Find why expo_indir is so much higher than EFSA toxicokinetic assessment. Make an alternative model.
- Add ERF for PFAS (sum of PFOS, PFHxS, PFOA, PFNA). Solution: Make a new page for ERF of PFAS and add that to adjusted ERFs. First immunology; postpone cholesterol and low birth weight and liver toxicity. DONE
- Add case burdens for PFAS outcomes. Solution: give rough estimates for immunology to get started. DONE
- Add PFAS concentrations to data. Solution: Look at Porvoo concentrations first and make conc_pfas; combine that with conc_vit. DONE
- Add dioxin and MeHg concentrations. Later.
- Now that the model runs technically, look through each part to check that it makes sense.
- If PAF contains some inputs with Age and some without Age (implying that they apply to any age group), only the explicit age groups remain in the end. Add RRorig to compensate for this. DONE
- TEQ and MeHg are missing from dose. Check why and correct.
objects.latest("Op_en2261", code_name="RR2") # RR on page [[Health impact assessment]]## Loading objects:
## RRRRorig <- RR@formula
RR@formula <- function(...) {
out <- RRorig()
out <- out * Ovariable(output=data.frame(Age=c(
"0 - 4", "5 - 9", "10 - 14", "15 - 19", "20 - 24", "25 - 29", "30 - 34", "35 - 39", "40 - 44", "45 - 49", "50 - 54",
"55 - 59", "60 - 64", "65 - 69", "70 - 74", "75 - 79", "80 - 84", "85+"),
Result=1),
marginal=c(TRUE,FALSE)
)
return(out)
}# This code was forked from https://github.com/jtuomist/fishhealth/blob/master/fishhealth.Rmd
# This code was previously forked from code Op_fi5923/model on page [[Kotimaisen kalan edistämisohjelma]]
# The code was even more previously forked from Op_fi5889/model on page [[Ruori]] and Op_en7748/model on page [[Goherr assessment]]
dat <- opbase.data("Op_fi5932", subset="Malliparametrit")[-1] # [[PFAS-yhdeisteiden tautitaakka]]
dec <- opbase.data("Op_fi5932", subset="Decisions")[-1]
DecisionTableParser(dec)
CTable <- opbase.data("Op_fi5932",subset="CollapseMarginals")
#for(i in 1:ncol(CTable)) {CTable[[i]] <- as.character(CTable[[i]])} # The default is currently character, not factor
CollapseTableParser(CTable)
cat("Laskennassa käytetty data.\n")## Laskennassa käytetty data.datcat("Tarkastellut päätökset.\n")## Tarkastellut päätökset.deccat("Aggregoidut marginaalit.\n")## Aggregoidut marginaalit.CTabledummy <- Ovariable("dummy",data=data.frame(Age="dummy", Fish="dummy", Compound="dummy", Area="dummy", Result=1)) # Keep these columns marginals
fish_proportion <- Ovariable( # How population subgroups eat fish differently
"fish_proportion",
data = data.frame(prepare(dat,"fish proportion",c("Type","Exposure_agent","Response","Unit"))),
unit="proportion of the mean")
total_amount <- Ovariable(
"total_amount",
data=prepare(dat, "amount", c("Type","Response","Exposure_agent","Unit")),
unit="M kg/a")
amount <- Ovariable(
"amount",
dependencies = data.frame(Name=c("total_amount", "fish_proportion")),
formula = function(...) {
amount <- total_amount
# Filleted weight, i.e. no loss.
amount <- amount * 1000 / 5.52 /365.25
# M kg/a per 5.52M population --> g/d per average person.
amount <- amount * fish_proportion
# fish_proportion tells the relative amount in each subgroup
# Match KKE-classification in amount with Fineli classification
tmp <- Ovariable(
output = data.frame(
Kala = c("Kasvatettu", "Kaupallinen", "Kirjolohi", "Silakka", "Vapaa-ajan", "Muu tuonti", "Tuontikirjolohi", "Tuontilohi"),
Fish = c("Whitefish", "Average fish","Rainbow trout", "Herring", "Average fish", "Average fish", "Rainbow trout", "Salmon"),
Result = 1
),
marginal = c(TRUE, TRUE, FALSE)
)
amount <- amount * tmp
return(amount)
},
unit="g/d"
)
# Exposure:To child and To eater not needed, because dioxins are not (yet) included
population <- Ovariable(
"population",
data=prepare(dat, "population", c("Type", "Exposure_agent", "Response","Unit")),
unit = "#"
)
incidence <- Ovariable(
"incidence",
data = prepare(dat,"incidence",c("Type","Exposure_agent","Unit")),
unit="1/person-year")
#incidence@data$Age[is.na(incidence@data$Age)] <- ""
case_burden <- Ovariable(
"case_burden",
data = prepare(dat,"case burden",c("Type", "Exposure_agent","Unit")),
unit="DALY/case")
ERFchoice <- Ovariable(
"ERFchoice",
data =
prepare(dat, "ERFchoice", c("Unit", "Type"))
)
InpBoD <- EvalOutput(Ovariable( # Evaluated because is not a dependency but an Input
"InpBoD",
data = prepare(dat, "BoD", c("Type","Exposure_agent","Unit")),
unit="DALY/a"
))
InpBoD$Response[InpBoD$Response=="All causes"] <- "All-cause mortality"
InpBoD$Response[InpBoD$Response=="Depressive disorders"] <- "Depression"
InpBoD$Response[InpBoD$Response=="Neoplasms"] <- "Cancer morbidity"
InpBoD$Response[InpBoD$Response=="Respiratory infections and tuberculosis"] <- "Immunosuppression" # Infections of 0-9-year-olds are assumed to represent the background BoD of immunosuppressive effect of PFAS
InpBoD$Response[InpBoD$Response=="Cardiovascular diseases"] <- "CHD2 mortality"conc_vit <- Ovariable(
"conc_vit",
ddata = "Op_en1838", # [[Concentrations of beneficial nutrients in fish]]
subset = "Fineli data for common fish species",
unit="ALA mg/g, DHA mg/g, Fish g/g, Omega3 mg/g, Vitamin D µg/g f.w. after adjustment"
)
df = conc_vit@data
df$Nutrient[df$Nutrient=="D-vitamiini (µg)"] <- "Vitamin D"
df$Nutrient[df$Nutrient=="rasvahapot n-3 moni-tyydyttymättömät (g)"] <- "Omega3"
df$Nutrient[df$Nutrient=="rasvahappo 18:3 n-3 (alfalinoleenihappo) (mg)"] <- "ALA"
df$Nutrient[df$Nutrient=="rasvahappo 22:6 n-3 (DHA) (mg)"] <- "DHA"
df$Nutrient[df$Nutrient=="proteiini (g)"] <- "Fish"
df$conc_vitResult[df$Nutrient=="Fish"] <- "1"
df <- dropall(df[df$Nutrient %in% c("Vitamin D", "Omega3", "ALA", "DHA", "Fish") , ])
conc_vit@data <- df
######## Concentration of PFAS
# Data from EU-kalat3 (Finland excl Vanhankaupunginlahti): # pg/g fresh weight
# POP mean sd min Q0.025 median Q0.975 max
# 2.5% PFOS 2055.757 1404.045 305.0399 330.1365 1533.269 5029.697 5814.935
# Data from EU-kalat3 (Vanhankaupunginlahti, Helsinki) # ng/g f.w.
# POP mean sd min Q0.025 median Q0.975 max
#2.5% PFOS 14.428 11.94542 1.499441 1.607789 15.64988 35.32517 38.91994
conc_eukalat <- EvalOutput(Ovariable(
"conc_eukalat",
data = data.frame(
Area = c("Suomi","Helsinki"),
Compound="PFOS",
Result=c("2.056 (3.301 - 5.030)", "14.428 (1.499 - 35.325)")),
unit="ng/g fresh weight"
))
conc_pfas_raw <- Ovariable(
"conc_pfas_raw",
ddata="Op_fi5932",subset="PFAS concentrations",
unit="ng/g f.w.")
conc_pfas <- Ovariable(
"conc_pfas",
dependencies = data.frame(Name="conc_pfas_raw"),
formula = function(...) {
out <- conc_pfas_raw
out$Source <- NULL
out$Area <- "Porvoo"
out <- expand_index(out, list(Fish=list(Perch=c(
"Average fish", "Pike","Rainbow trout","Roach", "Salmon", "Vendace", "Whitefish"))))
out@marginal <- c(TRUE, TRUE, FALSE, TRUE, TRUE)
return(out)
},
unit="ng/g fresh weight"
)
#sum_pfas <- oapply(conc_pfas, cols=c("Kala","Compound"), FUN=sum)
#tmp <- conc_pfas / sum_pfas
#summary(tmp, marginals="Compound")
#
## This tells that PFOS consists of 71 - 97 % of the four key PFAS, while PFOA, PFNA, and PFHxS consist of
# 0 - 10 %, 2 - 18 %, and 0 - 9 %, respectively.
# Even if we included the next most abundant congeners, i.e. PFDA and PFUnA, the overall picture would not change.
conc <- Ovariable(
"conc",
dependencies = data.frame(Name=c("conc_vit", "conc_pfas")),
formula = function(...){
conc_vit <- oapply(conc_vit, cols=c("Kala", "Adjust"),FUN=mean)
colnames(conc_vit@output)[colnames(conc_vit@output)=="Nutrient"] <- "Compound"
conc_pfas <- oapply(conc_pfas, cols=c("Obs","Area"), FUN=mean)
conc_pfas$Compound[conc_pfas$Compound %in% c("PFOA","PFNA","PFHxS","PFOS")] <- "PFAS"
conc_pfas <- oapply(conc_pfas, cols="", FUN=sum)
out <- OpasnetUtils::combine(conc_vit, conc_pfas)
return(out)
}
)###################################################################
# Code copied from http://en.opasnet.org/w/Goherr_assessment#
mc2dparam<- list(
N2 = 10, # Number of iterations in the new Iter
strength = 50, # Sample size to which the fun is to be applied. Resembles number of observations
run2d = FALSE, # Should the mc2d function be used or not?
info = 1, # Ovariable that contains additional indices, e.g. newmarginals.
newmarginals = c("Exposure"), # Names of columns that are non-marginals but should be sampled enough to become marginals
method = "bootstrap", # which method to use for 2D Monte Carlo? Currently bootsrap is the only option.
fun = mean # Function for aggregating the first Iter dimension.
)
if(FALSE) {
## Exposure with background exposure but without mother's exposure to child
expo_dir <- Ovariable(
"expo_dir",
dependencies=data.frame(Name=c("amount","conc","expo_bg")),
formula = function(...) {
out <- conc[conc$Exposure_agent=="TEQ",] * 0 + 1
out$Exposure_agent <- "Fish"
out <- combine(conc, out, name="conc")
out <- oapply(amount * out, cols="Fish", FUN=sum)
out <- Ovariable(output = data.frame(
Exposcen = c("BAU", "No exposure"),
Result = c(1, 0)
), marginal=c(TRUE,FALSE)) * out + expo_bg
out$Exposure <- as.factor(
ifelse(
out$Exposure_agent %in% c("DHA", "MeHg"),
"To child",
"To eater"
)
)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d; Fish: g /d"
)
## Background-exposure to vitamin D and omega-3
addexposure <- Ovariable(
"addexposure",
ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]]
subset = "Background exposure",
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Should the background be specific for gender and country? At the moment it is.
expo_bg <- Ovariable(
"expo_bg",
dependencies = data.frame(Name="addexposure","info"),
formula = function(...) {
out <- addexposure
# Empty values ("") in indices must be replaced by NA so that Ops works correctly.
levels(out$Gender)[levels(out$Gender) == ""] <- NA
levels(out$Country)[levels(out$Country) == ""] <- NA
levels(out$Exposure_agent)[levels(out$Exposure_agent) == ""] <- NA
out@output <- fillna(out@output, c("Country", "Gender", "Exposure_agent"))
temp1 <- out[out$Exposure_agent %in% c("PCDDF","PCB") , ]
temp1 <- oapply(temp1, cols = "Exposure_agent", FUN = sum)
temp1$Exposure_agent <- "TEQ"
temp2 <- out[out$Exposure_agent %in% c("EPA", "DHA") , ]
temp2 <- oapply(temp2, cols = "Exposure_agent", FUN = sum)
temp2$Exposure_agent <- "Omega3"
out <- OpasnetUtils::combine(out, temp1, temp2)
out <- unkeep(out * info, prevresults = TRUE, sources = TRUE)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Stores non-marginal columns for further use.
info <- Ovariable(
"info",
dependencies = data.frame(Name = c("jsp")),
formula = function(...) {
out <- jsp
out$Group <- factor(
paste(out$Gender, out$Ages),
levels = c("Female 18-45", "Male 18-45", "Female >45", "Male >45")
)
out$Country <- factor(out$Country, ordered=FALSE)
out <- unique(out@output[c("Iter","Country","Group","Gender","Row")])
out$Result <- 1
return(out)
}
)
} # END IF
############################### Code from Goherr assessment ends
expo_dir <- Ovariable(
"expo_dir",
dependencies = data.frame(Name=c("conc", "amount")),
formula = function(...) {
conc$Fish[conc$Fish %in% c("Perch","Pike-perch","Eel","")] <- "Average fish"
conc <- oapply(conc, cols="", FUN=mean)
out <- conc * amount
# colnames(out@output)[colnames(out@output)=="Nutrient"] <- "Exposure_agent"
return(out)
},
unit="ng/d"
)
exposure <- Ovariable(
"exposure",
dependencies = data.frame(
Name = c(
"expo_dir", # direct exposure, i.e. the person eats or breaths the exposure agent themself
"expo_indir", # indirect exposure, i.e. the person (typically fetus or infant) is exposed via someone else (mother)
"mc2d" # 2D Monte Carlo function
),
Ident = c(
NA,
"Op_en7797/expo_indir2", # [[Infant's dioxin exposure]] # expo_indir
"Op_en7805/mc2d") # [[Two-dimensional Monte Carlo]]
),
formula = function(...) {
out <- OpasnetUtils::combine(expo_dir, expo_indir)
out <- unkeep(out, "Source.1", sources=TRUE)
out <- mc2d(out)
out$Exposure[is.na(out$Exposure)] <- "Direct"
return(out)
},
unit = "PCDDF, PCB, TEQ: (To eater: pg /day; to child: pg /g fat); Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)
exposure <- Ovariable(
"exposure",
dependencies=data.frame(Name=c("amount","conc")),
formula = function(...) {
out <- amount * conc
colnames(out@output)[colnames(out@output)=="Compound"] <- "Exposure_agent"
return(out)
}
)objects.latest("Op_en2261",code_name="BoDattr2") # [[Health impact assessment]]## Loading objects:
## BoDattrtryCatch(BoDattr <- EvalOutput(BoDattr, verbose=TRUE))## Evaluating BoDattr ...
##
## - BoD fetched successfully!
##
## - PAF fetched successfully!
## - Evaluating BoD ...
## - - Evaluating incidence ...
##
## done(0.01 secs)!
## - - Checking incidence marginals ... Response, Age, incidenceSource recognized as marginal(s).## Loading required package: reshape2##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smiths## - - Processing incidence decisions ... done!
## - - Evaluating case_burden ...
##
## done(0 secs)!
## - - Checking case_burden marginals ... Response, case_burdenSource recognized as marginal(s).
## - - Processing case_burden marginal collapses ... done!
## - - Evaluating population ...
##
## done(0 secs)!
## - - Checking population marginals ... Gender, Age, populationSource recognized as marginal(s).
##
## - done(0.3 secs)!
## - Checking BoD marginals ... Response, Age, incidenceSource, Adjust, Gender, populationSource, BoDSource recognized as marginal(s).
## - Processing BoD inputs ... done!
## - Processing BoD marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying BoD, found NAs in indices: Adjust, InpBoDSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.## done!
## - Evaluating PAF ...
##
## - - dose fetched successfully!
##
## - - ERF fetched successfully!
##
## - - frexposed fetched successfully!
##
## - - P_illness fetched successfully!
##
## - - sumExposcen fetched successfully!
##
## - - mc2d fetched successfully!
## - - Evaluating dose ...
##
## - - - BW fetched successfully!
## - - - Evaluating exposure ...
## - - - - Evaluating amount ...
## - - - - - Evaluating total_amount ...
##
## done(0.01 secs)!
## - - - - - Checking total_amount marginals ... Kala, Scenario, total_amountSource recognized as marginal(s).
## - - - - - Evaluating fish_proportion ...
##
## done(0 secs)!
## - - - - - Checking fish_proportion marginals ... Gender, fish_proportionSource recognized as marginal(s).
##
## ---- done(0.14 secs)!
## - - - - Checking amount marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource recognized as marginal(s).
## - - - - Evaluating conc ...
## - - - - - Evaluating conc_vit ...
##
## done(0.01 secs)!
## - - - - - Checking conc_vit marginals ... Kala, Fish, Nutrient, conc_vitSource recognized as marginal(s).
## - - - - - Processing conc_vit decisions ... done!
## - - - - - Evaluating conc_pfas ...
## - - - - - - Evaluating conc_pfas_raw ...
##
## done(0.03 secs)!
## - - - - - - Checking conc_pfas_raw marginals ... Fish, Compound, conc_pfas_rawSource recognized as marginal(s).
##
## ----- done(0.09 secs)!
## - - - - - Checking conc_pfas marginals ... Fish, Compound, conc_pfas_rawSource, Area, conc_pfasSource recognized as marginal(s).
##
## ---- done(0.22 secs)!
## - - - - Checking conc marginals ... Fish, Compound, conc_pfas_rawSource, concSource recognized as marginal(s).
##
## --- done(0.46 secs)!
## - - - Checking exposure marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource, Exposure_agent, conc_pfas_rawSource, concSource, exposureSource recognized as marginal(s).
## - - - Processing exposure marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying exposure, found NAs in indices: conc_pfas_rawSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.## done!
## - - - Evaluating BW ...
##
## done(0 secs)!
## - - - Checking BW marginals ... BWSource recognized as marginal(s).
##
## -- done(25.3 secs)!
## - - Checking dose marginals ... Scenario, total_amountSource, Gender, fish_proportionSource, amountSource, Exposure_agent, conc_pfas_rawSource, concSource, Scaling, exposureSource, BWSource, doseSource recognized as marginal(s).
## - - Processing dose marginal collapses ... done!
## - - Evaluating ERF ...
##
## - - - ERF_env fetched successfully!
##
## - - - ERF_omega3 fetched successfully!
##
## - - - ERF_mehg fetched successfully!
##
## - - - ERF_diox fetched successfully!
##
## - - - ERF_vit fetched successfully!
##
## - - - ERF_micr fetched successfully!
##
## - - - ERF_pfas fetched successfully!
## - - - Evaluating ERF_env ...
##
## done(0.02 secs)!
## - - - Checking ERF_env marginals ... Exposure_agent, Response, Subgroup, Exposure, ER_function, Scaling, Exposure_unit, Observation, ERF_envSource recognized as marginal(s).
## - - - Evaluating ERF_omega3 ...
##
## done(0 secs)!
## - - - Checking ERF_omega3 marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_omega3Source recognized as marginal(s).
## - - - Evaluating ERF_mehg ...
##
## done(0 secs)!
## - - - Checking ERF_mehg marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_mehgSource recognized as marginal(s).
## - - - Evaluating ERF_diox ...
##
## done(0 secs)!
## - - - Checking ERF_diox marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_dioxSource recognized as marginal(s).
## - - - Evaluating ERF_vit ...
##
## done(0 secs)!
## - - - Checking ERF_vit marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_vitSource recognized as marginal(s).
## - - - Evaluating ERF_micr ...
##
## done(0 secs)!
## - - - Checking ERF_micr marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_micrSource recognized as marginal(s).
## - - - Evaluating ERF_pfas ...
##
## done(0 secs)!
## - - - Checking ERF_pfas marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_pfasSource recognized as marginal(s).
## - - - Evaluating ERFchoice ...
##
## done(0 secs)!
## - - - Checking ERFchoice marginals ... Exposure_agent, Response, Scaling, Exposure, ER_function, ERFchoiceSource recognized as marginal(s).
##
## -- done(2.92 mins)!
## - - Checking ERF marginals ... Exposure_agent, Response, Exposure, ER_function, Scaling, Observation, ERFSource recognized as marginal(s).
## - - Processing ERF marginal collapses ... done!
## - - Evaluating RR ...
## - - - Processing dose marginal collapses ... done!
## - - - Processing ERF marginal collapses ... done!
##
## -- done(0.16 secs)!
## - - Checking RR marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, Scenario, total_amountSource, Gender, fish_proportionSource, amountSource, conc_pfas_rawSource, doseSource, Age, RRSource recognized as marginal(s).
## - - Evaluating frexposed ...
##
## done(0 secs)!
## - - Checking frexposed marginals ... frexposedSource recognized as marginal(s).
## - - Evaluating P_illness ...
##
## done(0 secs)!
## - - Checking P_illness marginals ... Response, Illness, Age, P_illnessSource recognized as marginal(s).
##
## - done(5.85 mins)!
## - Checking PAF marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, Scenario, total_amountSource, Gender, fish_proportionSource, amountSource, conc_pfas_rawSource, doseSource, frexposedSource, Age, incidenceSource, Adjust, RRSource, PAFSource recognized as marginal(s).
## - Processing PAF marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying PAF, found NAs in indices: Adjust. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.## done!
##
## done(6.66 mins)!
## Checking BoDattr marginals ... Response, Age, Gender, Adjust, InpBoDSource, Exposure_agent, Scenario, total_amountSource, fish_proportionSource, amountSource, conc_pfas_rawSource, PAFSource, BoDattrSource recognized as marginal(s).oprint(summary(amount,"mean"))## Kala Scenario Gender Fish mean
## 1 Kaupallinen BAU Female Average fish 1.2697279
## 2 Muu tuonti BAU Female Average fish 11.1101191
## 3 Vapaa-ajan BAU Female Average fish 3.9282207
## 4 Kaupallinen BAU Male Average fish 1.9045919
## 5 Muu tuonti BAU Male Average fish 16.6651787
## 6 Vapaa-ajan BAU Male Average fish 5.8923310
## 7 Silakka BAU Female Herring 0.6348640
## 8 Silakka BAU Male Herring 0.9522959
## 9 Kirjolohi BAU Female Rainbow trout 2.6584928
## 10 Tuontikirjolohi BAU Female Rainbow trout 1.9045919
## 11 Kirjolohi BAU Male Rainbow trout 3.9877392
## 12 Tuontikirjolohi BAU Male Rainbow trout 2.8568878
## 13 Tuontilohi BAU Female Salmon 9.3642433
## 14 Tuontilohi BAU Male Salmon 14.0463649
## 15 Kasvatettu BAU Female Whitefish 0.2380740
## 16 Kasvatettu BAU Male Whitefish 0.3571110oprint(summary(BoD,marginals=c("Age","Response"),"mean"))## Age Response mean
## 1 0 - 4 Cancer morbidity 309.5200
## 2 10 - 14 Cancer morbidity 347.0750
## 3 15 - 19 Cancer morbidity 414.9750
## 4 20 - 24 Cancer morbidity 569.5250
## 5 25 - 29 Cancer morbidity 795.4550
## 6 30 - 34 Cancer morbidity 1147.9850
## 7 35 - 39 Cancer morbidity 1679.1250
## 8 40 - 44 Cancer morbidity 2452.5650
## 9 45 - 49 Cancer morbidity 3849.3800
## 10 5 - 9 Cancer morbidity 345.5200
## 11 50 - 54 Cancer morbidity 6987.9000
## 12 55 - 59 Cancer morbidity 11237.3300
## 13 60 - 64 Cancer morbidity 16331.9100
## 14 65 - 69 Cancer morbidity 21899.6500
## 15 70 - 74 Cancer morbidity 25551.8600
## 16 75 - 79 Cancer morbidity 18015.2300
## 17 80 - 84 Cancer morbidity 14650.9150
## 18 85 - 89 Cancer morbidity 8413.0550
## 19 90 - 94 Cancer morbidity 3537.1750
## 20 Undefined Dioxin recommendation tolerable daily intake 305.4067
## 21 Undefined Dioxin recommendation tolerable daily intake 2018 888.4560
## 22 0 - 4 Immunosuppression 295.9850
## 23 5 - 9 Immunosuppression 261.5350
## 24 0 - 4 Loss in child's IQ points 16778.3774
## 25 Undefined PFAS TWI 2776.4249
## 26 0 - 4 Sperm concentration 4478.6700
## 27 Undefined Vitamin D recommendation 610.8135
## 28 0 - 4 Yes or no dental defect 343.9619
## 29 0 - 4 All-cause mortality 4867.4650
## 30 10 - 14 All-cause mortality 1085.5450
## 31 15 - 19 All-cause mortality 2965.9450
## 32 20 - 24 All-cause mortality 5274.8450
## 33 25 - 29 All-cause mortality 6263.5400
## 34 30 - 34 All-cause mortality 6710.3100
## 35 35 - 39 All-cause mortality 8275.6450
## 36 40 - 44 All-cause mortality 10232.0150
## 37 45 - 49 All-cause mortality 13691.2800
## 38 5 - 9 All-cause mortality 931.1250
## 39 50 - 54 All-cause mortality 21321.8650
## 40 55 - 59 All-cause mortality 29861.9100
## 41 60 - 64 All-cause mortality 39200.8750
## 42 65 - 69 All-cause mortality 49295.5050
## 43 70 - 74 All-cause mortality 59799.7000
## 44 75 - 79 All-cause mortality 49428.5150
## 45 80 - 84 All-cause mortality 51968.9150
## 46 85 - 89 All-cause mortality 42765.3450
## 47 90 - 94 All-cause mortality 26756.6400
## 48 0 - 4 Depression 0.4150
## 49 10 - 14 Depression 555.8850
## 50 15 - 19 Depression 1320.8250
## 51 20 - 24 Depression 1841.0150
## 52 25 - 29 Depression 1801.4650
## 53 30 - 34 Depression 1590.0200
## 54 35 - 39 Depression 1721.7250
## 55 40 - 44 Depression 1700.2800
## 56 45 - 49 Depression 1534.1700
## 57 5 - 9 Depression 76.4850
## 58 50 - 54 Depression 1718.9550
## 59 55 - 59 Depression 1767.1650
## 60 60 - 64 Depression 1676.7400
## 61 65 - 69 Depression 1561.5750
## 62 70 - 74 Depression 1426.2350
## 63 75 - 79 Depression 799.7000
## 64 80 - 84 Depression 581.2950
## 65 85 - 89 Depression 363.0750
## 66 90 - 94 Depression 186.1150
## 67 0 - 4 CHD2 mortality 53.6100
## 68 10 - 14 CHD2 mortality 75.3150
## 69 15 - 19 CHD2 mortality 145.3450
## 70 20 - 24 CHD2 mortality 254.1000
## 71 25 - 29 CHD2 mortality 437.6450
## 72 30 - 34 CHD2 mortality 636.4200
## 73 35 - 39 CHD2 mortality 1168.2600
## 74 40 - 44 CHD2 mortality 1977.5650
## 75 45 - 49 CHD2 mortality 3117.3100
## 76 5 - 9 CHD2 mortality 48.7800
## 77 50 - 54 CHD2 mortality 5476.2900
## 78 55 - 59 CHD2 mortality 8747.4650
## 79 60 - 64 CHD2 mortality 13077.9700
## 80 65 - 69 CHD2 mortality 18100.7500
## 81 70 - 74 CHD2 mortality 24549.2850
## 82 75 - 79 CHD2 mortality 23413.4850
## 83 80 - 84 CHD2 mortality 27899.3350
## 84 85 - 89 CHD2 mortality 25240.0350
## 85 90 - 94 CHD2 mortality 16630.6150
## 86 15 - 19 Breast cancer 2.0900
## 87 20 - 24 Breast cancer 7.7850
## 88 25 - 29 Breast cancer 36.8200
## 89 30 - 34 Breast cancer 145.1600
## 90 35 - 39 Breast cancer 263.6000
## 91 40 - 44 Breast cancer 433.3500
## 92 45 - 49 Breast cancer 704.9550
## 93 50 - 54 Breast cancer 1045.3250
## 94 55 - 59 Breast cancer 1341.8150
## 95 60 - 64 Breast cancer 1513.2150
## 96 65 - 69 Breast cancer 1649.3300
## 97 70 - 74 Breast cancer 1678.0350
## 98 75 - 79 Breast cancer 1232.0100
## 99 80 - 84 Breast cancer 950.8800
## 100 85 - 89 Breast cancer 578.1900
## 101 90 - 94 Breast cancer 296.1800oprint(summary(BoDattr,marginals=c("Age","Response"),"mean"))## Age Response mean
## 1 0 - 4 Immunosuppression 6.818641e+00
## 2 5 - 9 Immunosuppression 7.294478e+00
## 3 0 - 4 Loss in child's IQ points -3.292014e+03
## 4 Undefined PFAS TWI 1.370935e+03
## 5 Undefined Vitamin D recommendation 6.108135e+02
## 6 0 - 4 All-cause mortality -3.940215e+02
## 7 10 - 14 All-cause mortality -8.729181e+01
## 8 15 - 19 All-cause mortality -2.510075e+02
## 9 20 - 24 All-cause mortality -4.582433e+02
## 10 25 - 29 All-cause mortality -5.472993e+02
## 11 30 - 34 All-cause mortality -5.805901e+02
## 12 35 - 39 All-cause mortality -7.164739e+02
## 13 40 - 44 All-cause mortality -8.793680e+02
## 14 45 - 49 All-cause mortality -1.166310e+03
## 15 5 - 9 All-cause mortality -7.424681e+01
## 16 50 - 54 All-cause mortality -1.808052e+03
## 17 55 - 59 All-cause mortality -2.535812e+03
## 18 60 - 64 All-cause mortality -3.319267e+03
## 19 65 - 69 All-cause mortality -4.145066e+03
## 20 70 - 74 All-cause mortality -4.980650e+03
## 21 75 - 79 All-cause mortality -4.044850e+03
## 22 80 - 84 All-cause mortality -4.134937e+03
## 23 0 - 4 Depression -7.647127e-02
## 24 10 - 14 Depression -9.937062e+01
## 25 15 - 19 Depression -2.347323e+02
## 26 20 - 24 Depression -3.276647e+02
## 27 25 - 29 Depression -3.226701e+02
## 28 30 - 34 Depression -2.865779e+02
## 29 35 - 39 Depression -3.110681e+02
## 30 40 - 44 Depression -3.073361e+02
## 31 45 - 49 Depression -2.766914e+02
## 32 5 - 9 Depression -1.387565e+01
## 33 50 - 54 Depression -3.090770e+02
## 34 55 - 59 Depression -3.166728e+02
## 35 60 - 64 Depression -2.997941e+02
## 36 65 - 69 Depression -2.791761e+02
## 37 70 - 74 Depression -2.551780e+02
## 38 75 - 79 Depression -1.425020e+02
## 39 80 - 84 Depression -1.024717e+02
## 40 0 - 4 CHD2 mortality -8.359787e+00
## 41 10 - 14 CHD2 mortality -1.170566e+01
## 42 15 - 19 CHD2 mortality -2.262663e+01
## 43 20 - 24 CHD2 mortality -3.966234e+01
## 44 25 - 29 CHD2 mortality -6.844631e+01
## 45 30 - 34 CHD2 mortality -9.964232e+01
## 46 35 - 39 CHD2 mortality -1.833859e+02
## 47 40 - 44 CHD2 mortality -3.106654e+02
## 48 45 - 49 CHD2 mortality -4.899542e+02
## 49 5 - 9 CHD2 mortality -7.579246e+00
## 50 50 - 54 CHD2 mortality -8.613391e+02
## 51 55 - 59 CHD2 mortality -1.375930e+03
## 52 60 - 64 CHD2 mortality -2.055932e+03
## 53 65 - 69 CHD2 mortality -2.841726e+03
## 54 70 - 74 CHD2 mortality -3.847268e+03
## 55 75 - 79 CHD2 mortality -3.660036e+03
## 56 80 - 84 CHD2 mortality -4.346514e+03
## 57 15 - 19 Breast cancer -4.624153e-01
## 58 20 - 24 Breast cancer -1.587543e+00
## 59 25 - 29 Breast cancer -7.326368e+00
## 60 30 - 34 Breast cancer -2.873340e+01
## 61 35 - 39 Breast cancer -5.218217e+01
## 62 40 - 44 Breast cancer -8.577757e+01
## 63 45 - 49 Breast cancer -1.395958e+02
## 64 50 - 54 Breast cancer -2.070689e+02
## 65 55 - 59 Breast cancer -2.658472e+02
## 66 60 - 64 Breast cancer -2.998331e+02
## 67 65 - 69 Breast cancer -3.267095e+02
## 68 70 - 74 Breast cancer -3.327787e+02
## 69 75 - 79 Breast cancer -2.441402e+02
## 70 80 - 84 Breast cancer -1.884558e+02oprint(summary(BoDattr,marginals=c("Exposure_agent","Response"),"mean"))## Exposure_agent Response mean
## 1 PFAS Immunosuppression 7.056559
## 2 DHA Loss in child's IQ points -3292.013959
## 3 PFAS PFAS TWI 1370.934688
## 4 Vitamin D Vitamin D recommendation 610.813471
## 5 Fish All-cause mortality -1771.969747
## 6 Fish Depression -228.525606
## 7 Omega3 CHD2 mortality -1190.045495
## 8 Omega3 Breast cancer -155.749901oprint(summary(BoDattr,marginals=c("Gender","Response"),"mean"))## Gender Response mean
## 1 Female Immunosuppression 5.205862
## 2 Male Immunosuppression 8.907256
## 3 Female Loss in child's IQ points -2561.773339
## 4 Male Loss in child's IQ points -4022.254579
## 5 Female PFAS TWI 0.000000
## 6 Male PFAS TWI 2741.869376
## 7 Female Vitamin D recommendation 618.415679
## 8 Male Vitamin D recommendation 603.211263
## 9 Female All-cause mortality -1003.248948
## 10 Male All-cause mortality -2540.690546
## 11 Female Depression -235.343301
## 12 Male Depression -221.707910
## 13 Female CHD2 mortality -815.643510
## 14 Male CHD2 mortality -1564.447480
## 15 Female Breast cancer -308.799252
## 16 Male Breast cancer -2.700550oprint(summary(case_burden,"mean"))## Response mean
## 1 Dioxin recommendation tolerable daily intake 0.001004988
## 2 Dioxin recommendation tolerable daily intake 2018 0.001004988
## 3 Loss in child's IQ points 0.110000000
## 4 PFAS TWI 0.001004988
## 5 Sperm concentration 2.500000000
## 6 Vitamin D recommendation 0.001004988
## 7 Yes or no dental defect 0.060000000oprint(summary(conc,"mean"))## Fish Compound mean
## 1 Average fish ALA 0.690000
## 2 Bream ALA 0.220000
## 3 Herring ALA 1.740000
## 4 Pike ALA 0.080000
## 5 Rainbow trout ALA 4.810000
## 6 Roach ALA 0.100000
## 7 Salmon ALA 7.960000
## 8 Vendace ALA 1.350000
## 9 Whitefish ALA 2.220000
## 10 Average fish DHA 2.540000
## 11 Bream DHA 2.730000
## 12 Herring DHA 5.860000
## 13 Pike DHA 0.300000
## 14 Rainbow trout DHA 7.570000
## 15 Roach DHA 2.870000
## 16 Salmon DHA 6.690000
## 17 Vendace DHA 3.000000
## 18 Whitefish DHA 3.940000
## 19 Average fish Fish 1.000000
## 20 Bream Fish 1.000000
## 21 Herring Fish 1.000000
## 22 Pike Fish 1.000000
## 23 Rainbow trout Fish 1.000000
## 24 Roach Fish 1.000000
## 25 Salmon Fish 1.000000
## 26 Vendace Fish 1.000000
## 27 Whitefish Fish 1.000000
## 28 Average fish Omega3 7.000000
## 29 Bream Omega3 6.000000
## 30 Herring Omega3 24.000000
## 31 Pike Omega3 0.500000
## 32 Rainbow trout Omega3 18.000000
## 33 Roach Omega3 5.000000
## 34 Salmon Omega3 23.000000
## 35 Vendace Omega3 10.000000
## 36 Whitefish Omega3 10.000000
## 37 Average fish PFAS 8.793125
## 38 Bream PFAS 4.950000
## 39 Eel PFAS 7.895000
## 40 Herring PFAS 1.655000
## 41 Perch PFAS 8.793125
## 42 Pike PFAS 8.793125
## 43 Pike-perch PFAS 2.595000
## 44 Rainbow trout PFAS 8.793125
## 45 Roach PFAS 8.793125
## 46 Salmon PFAS 8.793125
## 47 Vendace PFAS 8.793125
## 48 Whitefish PFAS 8.793125
## 49 Average fish Vitamin D 0.105000
## 50 Bream Vitamin D 0.140000
## 51 Herring Vitamin D 0.156000
## 52 Pike Vitamin D 0.021000
## 53 Rainbow trout Vitamin D 0.051000
## 54 Roach Vitamin D 0.100000
## 55 Salmon Vitamin D 0.067000
## 56 Vendace Vitamin D 0.094000
## 57 Whitefish Vitamin D 0.144000oprint(summary(dose,"mean"))## Scenario Gender Exposure_agent Scaling mean
## 1 BAU Female ALA BW 1.56247954
## 2 BAU Male ALA BW 2.34371931
## 3 BAU Female DHA BW 2.04671635
## 4 BAU Male DHA BW 3.07007453
## 5 BAU Female Fish BW 0.44440477
## 6 BAU Male Fish BW 0.66660715
## 7 BAU Female Omega3 BW 6.13267239
## 8 BAU Male Omega3 BW 9.19900859
## 9 BAU Female PFAS BW 3.84296754
## 10 BAU Male PFAS BW 5.76445130
## 11 BAU Female Vitamin D BW 0.03865415
## 12 BAU Male Vitamin D BW 0.05798122
## 13 BAU Female ALA Log10 2.03891238
## 14 BAU Male ALA Log10 2.21500364
## 15 BAU Female DHA Log10 2.15615570
## 16 BAU Male DHA Log10 2.33224696
## 17 BAU Female Fish Log10 1.49287675
## 18 BAU Male Fish Log10 1.66896801
## 19 BAU Female Omega3 Log10 2.63274781
## 20 BAU Male Omega3 Log10 2.80883906
## 21 BAU Female PFAS Log10 2.42976476
## 22 BAU Male PFAS Log10 2.60585601
## 23 BAU Female Vitamin D Log10 0.43229411
## 24 BAU Male Vitamin D Log10 0.60838537
## 25 BAU Female ALA None 109.37356784
## 26 BAU Male ALA None 164.06035175
## 27 BAU Female DHA None 143.27014453
## 28 BAU Male DHA None 214.90521680
## 29 BAU Female Fish None 31.10833358
## 30 BAU Male Fish None 46.66250037
## 31 BAU Female Omega3 None 429.28706762
## 32 BAU Male Omega3 None 643.93060143
## 33 BAU Female PFAS None 269.00772748
## 34 BAU Male PFAS None 403.51159123
## 35 BAU Female Vitamin D None 2.70579016
## 36 BAU Male Vitamin D None 4.05868524oprint(summary(ERF,"mean"))## Exposure_agent Response
## 1 Fish All-cause mortality
## 2 Omega3 Breast cancer
## 3 Fish Depression
## 4 DHA Loss in child's IQ points
## 5 TEQ Sperm concentration
## 6 TEQ Yes or no dental defect
## 7 Omega3 CHD2 mortality
## 8 Vitamin D Vitamin D recommendation
## 9 MeHg Loss in child's IQ points
## 10 PFAS Immunosuppression
## 11 PFAS PFAS TWI
## 12 TEQ Cancer morbidity
## 13 TEQ Dioxin recommendation tolerable daily intake
## 14 TEQ Dioxin recommendation tolerable daily intake 2018
## 15 Fish All-cause mortality
## 16 Omega3 Breast cancer
## 17 Fish Depression
## 18 DHA Loss in child's IQ points
## 19 TEQ Sperm concentration
## 20 TEQ Yes or no dental defect
## 21 Omega3 CHD2 mortality
## 22 Vitamin D Vitamin D recommendation
## 23 MeHg Loss in child's IQ points
## 24 PFAS Immunosuppression
## 25 PFAS PFAS TWI
## 26 TEQ Cancer morbidity
## 27 TEQ Dioxin recommendation tolerable daily intake
## 28 TEQ Dioxin recommendation tolerable daily intake 2018
## ER_function Scaling Observation mean
## 1 RR None ERF 0.997871700
## 2 RR None ERF 0.999487200
## 3 RR None ERF 0.994690400
## 4 ERS None ERF -0.001300000
## 5 ERS None ERF 0.000060000
## 6 ERS None ERF 0.001390971
## 7 Relative Hill None ERF -0.170000000
## 8 Step None ERF 100.000000000
## 9 ERS BW ERF 9.800000000
## 10 ERS BW ERF 0.022700000
## 11 TWI BW ERF 4.400000000
## 12 CSF BW ERF 0.000500000
## 13 TDI BW ERF 2.000000000
## 14 TDI BW ERF 0.288900000
## 15 RR None Threshold 0.000000000
## 16 RR None Threshold 0.000000000
## 17 RR None Threshold 0.000000000
## 18 ERS None Threshold 0.000000000
## 19 ERS None Threshold 0.000000000
## 20 ERS None Threshold 0.000000000
## 21 Relative Hill None Threshold 47.000000000
## 22 Step None Threshold 10.000000000
## 23 ERS BW Threshold 0.000000000
## 24 ERS BW Threshold 0.000000000
## 25 TWI BW Threshold 0.000000000
## 26 CSF BW Threshold 0.000000000
## 27 TDI BW Threshold 0.000000000
## 28 TDI BW Threshold 0.000000000oprint(summary(expo_dir,"mean"))## Warning in oapply(conc, cols = "", FUN = mean): While oapplying conc, found NAs
## in indices: conc_pfas_rawSource. They were automatically filled using fillna,
## which may result in a multiplied population. Please check your ovariable before
## using oapply.## Fish Compound Kala Scenario Gender mean
## 1 Whitefish ALA Kasvatettu BAU Female 0.52852424
## 2 Whitefish DHA Kasvatettu BAU Female 0.93801149
## 3 Whitefish Fish Kasvatettu BAU Female 0.23807398
## 4 Whitefish Omega3 Kasvatettu BAU Female 2.38073981
## 5 Whitefish PFAS Kasvatettu BAU Female 2.09341428
## 6 Whitefish Vitamin D Kasvatettu BAU Female 0.03428265
## 7 Average fish ALA Kaupallinen BAU Female 0.87611225
## 8 Average fish DHA Kaupallinen BAU Female 3.22510887
## 9 Average fish Fish Kaupallinen BAU Female 1.26972790
## 10 Average fish Omega3 Kaupallinen BAU Female 8.88809531
## 11 Average fish PFAS Kaupallinen BAU Female 8.91229950
## 12 Average fish Vitamin D Kaupallinen BAU Female 0.13332143
## 13 Rainbow trout ALA Kirjolohi BAU Female 12.78735034
## 14 Rainbow trout DHA Kirjolohi BAU Female 20.12479045
## 15 Rainbow trout Fish Kirjolohi BAU Female 2.65849279
## 16 Rainbow trout Omega3 Kirjolohi BAU Female 47.85287028
## 17 Rainbow trout PFAS Kirjolohi BAU Female 23.37645944
## 18 Rainbow trout Vitamin D Kirjolohi BAU Female 0.13558313
## 19 Average fish ALA Muu tuonti BAU Female 7.66598220
## 20 Average fish DHA Muu tuonti BAU Female 28.21970261
## 21 Average fish Fish Muu tuonti BAU Female 11.11011914
## 22 Average fish Omega3 Muu tuonti BAU Female 77.77083395
## 23 Average fish PFAS Muu tuonti BAU Female 77.98262060
## 24 Average fish Vitamin D Muu tuonti BAU Female 1.16656251
## 25 Herring ALA Silakka BAU Female 1.10466327
## 26 Herring DHA Silakka BAU Female 3.72030275
## 27 Herring Fish Silakka BAU Female 0.63486395
## 28 Herring Omega3 Silakka BAU Female 15.23673482
## 29 Herring PFAS Silakka BAU Female 1.05069984
## 30 Herring Vitamin D Silakka BAU Female 0.09903878
## 31 Rainbow trout ALA Tuontikirjolohi BAU Female 9.16108681
## 32 Rainbow trout DHA Tuontikirjolohi BAU Female 14.41776032
## 33 Rainbow trout Fish Tuontikirjolohi BAU Female 1.90459185
## 34 Rainbow trout Omega3 Tuontikirjolohi BAU Female 34.28265333
## 35 Rainbow trout PFAS Tuontikirjolohi BAU Female 16.74731423
## 36 Rainbow trout Vitamin D Tuontikirjolohi BAU Female 0.09713418
## 37 Salmon ALA Tuontilohi BAU Female 74.53937644
## 38 Salmon DHA Tuontilohi BAU Female 62.64678749
## 39 Salmon Fish Tuontilohi BAU Female 9.36424327
## 40 Salmon Omega3 Tuontilohi BAU Female 215.37759525
## 41 Salmon PFAS Tuontilohi BAU Female 82.34096162
## 42 Salmon Vitamin D Tuontilohi BAU Female 0.62740430
## 43 Average fish ALA Vapaa-ajan BAU Female 2.71047228
## 44 Average fish DHA Vapaa-ajan BAU Female 9.97768056
## 45 Average fish Fish Vapaa-ajan BAU Female 3.92822069
## 46 Average fish Omega3 Vapaa-ajan BAU Female 27.49754486
## 47 Average fish PFAS Vapaa-ajan BAU Female 27.57242657
## 48 Average fish Vitamin D Vapaa-ajan BAU Female 0.41246317
## 49 Whitefish ALA Kasvatettu BAU Male 0.79278636
## 50 Whitefish DHA Kasvatettu BAU Male 1.40701723
## 51 Whitefish Fish Kasvatettu BAU Male 0.35711097
## 52 Whitefish Omega3 Kasvatettu BAU Male 3.57110972
## 53 Whitefish PFAS Kasvatettu BAU Male 3.14012142
## 54 Whitefish Vitamin D Kasvatettu BAU Male 0.05142398
## 55 Average fish ALA Kaupallinen BAU Male 1.31416838
## 56 Average fish DHA Kaupallinen BAU Male 4.83766330
## 57 Average fish Fish Kaupallinen BAU Male 1.90459185
## 58 Average fish Omega3 Kaupallinen BAU Male 13.33214296
## 59 Average fish PFAS Kaupallinen BAU Male 13.36844925
## 60 Average fish Vitamin D Kaupallinen BAU Male 0.19998214
## 61 Rainbow trout ALA Kirjolohi BAU Male 19.18102550
## 62 Rainbow trout DHA Kirjolohi BAU Male 30.18718567
## 63 Rainbow trout Fish Kirjolohi BAU Male 3.98773919
## 64 Rainbow trout Omega3 Kirjolohi BAU Male 71.77930542
## 65 Rainbow trout PFAS Kirjolohi BAU Male 35.06468916
## 66 Rainbow trout Vitamin D Kirjolohi BAU Male 0.20337470
## 67 Average fish ALA Muu tuonti BAU Male 11.49897331
## 68 Average fish DHA Muu tuonti BAU Male 42.32955391
## 69 Average fish Fish Muu tuonti BAU Male 16.66517870
## 70 Average fish Omega3 Muu tuonti BAU Male 116.65625093
## 71 Average fish PFAS Muu tuonti BAU Male 116.97393090
## 72 Average fish Vitamin D Muu tuonti BAU Male 1.74984376
## 73 Herring ALA Silakka BAU Male 1.65699491
## 74 Herring DHA Silakka BAU Male 5.58045413
## 75 Herring Fish Silakka BAU Male 0.95229593
## 76 Herring Omega3 Silakka BAU Male 22.85510222
## 77 Herring PFAS Silakka BAU Male 1.57604976
## 78 Herring Vitamin D Silakka BAU Male 0.14855816
## 79 Rainbow trout ALA Tuontikirjolohi BAU Male 13.74163021
## 80 Rainbow trout DHA Tuontikirjolohi BAU Male 21.62664048
## 81 Rainbow trout Fish Tuontikirjolohi BAU Male 2.85688778
## 82 Rainbow trout Omega3 Tuontikirjolohi BAU Male 51.42398000
## 83 Rainbow trout PFAS Tuontikirjolohi BAU Male 25.12097134
## 84 Rainbow trout Vitamin D Tuontikirjolohi BAU Male 0.14570128
## 85 Salmon ALA Tuontilohi BAU Male 111.80906467
## 86 Salmon DHA Tuontilohi BAU Male 93.97018123
## 87 Salmon Fish Tuontilohi BAU Male 14.04636491
## 88 Salmon Omega3 Tuontilohi BAU Male 323.06639288
## 89 Salmon PFAS Tuontilohi BAU Male 123.51144243
## 90 Salmon Vitamin D Tuontilohi BAU Male 0.94110645
## 91 Average fish ALA Vapaa-ajan BAU Male 4.06570842
## 92 Average fish DHA Vapaa-ajan BAU Male 14.96652085
## 93 Average fish Fish Vapaa-ajan BAU Male 5.89233104
## 94 Average fish Omega3 Vapaa-ajan BAU Male 41.24631729
## 95 Average fish PFAS Vapaa-ajan BAU Male 41.35863985
## 96 Average fish Vitamin D Vapaa-ajan BAU Male 0.61869476#oprint(summary(exposure,"mean"))
#oprint(summary(fish_proportion,"mean"))
oprint(summary(incidence,"mean"))## Response Age Adjust mean
## 1 Immunosuppression 0 - 4 BAU 4.8100
## 2 Loss in child's IQ points 0 - 4 BAU 1.1920
## 3 Sperm concentration 0 - 4 BAU 0.0140
## 4 Yes or no dental defect 0 - 4 BAU 0.0448
## 5 Immunosuppression 5 - 9 BAU 3.9500
## 6 Dioxin recommendation tolerable daily intake Undefined BAU 0.1100
## 7 Dioxin recommendation tolerable daily intake 2018 Undefined BAU 0.3200
## 8 PFAS TWI Undefined BAU 1.0000
## 9 Vitamin D recommendation Undefined BAU 0.2200oprint(summary(PAF,"mean"))## Exposure_agent Response Scenario Gender Age Adjust
## 1 DHA Loss in child's IQ points BAU Female 0 - 4 BAU
## 2 PFAS Immunosuppression BAU Female 0 - 4 BAU
## 3 Fish All-cause mortality BAU Female 0 - 4 BAU
## 4 Omega3 Breast cancer BAU Female 0 - 4 BAU
## 5 Fish Depression BAU Female 0 - 4 BAU
## 6 Omega3 CHD2 mortality BAU Female 0 - 4 BAU
## 7 DHA Loss in child's IQ points BAU Male 0 - 4 BAU
## 8 PFAS Immunosuppression BAU Male 0 - 4 BAU
## 9 Fish All-cause mortality BAU Male 0 - 4 BAU
## 10 Omega3 Breast cancer BAU Male 0 - 4 BAU
## 11 Fish Depression BAU Male 0 - 4 BAU
## 12 Omega3 CHD2 mortality BAU Male 0 - 4 BAU
## 13 Fish All-cause mortality BAU Female 10 - 14 BAU
## 14 Omega3 Breast cancer BAU Female 10 - 14 BAU
## 15 Fish Depression BAU Female 10 - 14 BAU
## 16 Omega3 CHD2 mortality BAU Female 10 - 14 BAU
## 17 Fish All-cause mortality BAU Male 10 - 14 BAU
## 18 Omega3 Breast cancer BAU Male 10 - 14 BAU
## 19 Fish Depression BAU Male 10 - 14 BAU
## 20 Omega3 CHD2 mortality BAU Male 10 - 14 BAU
## 21 Fish All-cause mortality BAU Female 15 - 19 BAU
## 22 Omega3 Breast cancer BAU Female 15 - 19 BAU
## 23 Fish Depression BAU Female 15 - 19 BAU
## 24 Omega3 CHD2 mortality BAU Female 15 - 19 BAU
## 25 Fish All-cause mortality BAU Male 15 - 19 BAU
## 26 Omega3 Breast cancer BAU Male 15 - 19 BAU
## 27 Fish Depression BAU Male 15 - 19 BAU
## 28 Omega3 CHD2 mortality BAU Male 15 - 19 BAU
## 29 Fish All-cause mortality BAU Female 20 - 24 BAU
## 30 Omega3 Breast cancer BAU Female 20 - 24 BAU
## 31 Fish Depression BAU Female 20 - 24 BAU
## 32 Omega3 CHD2 mortality BAU Female 20 - 24 BAU
## 33 Fish All-cause mortality BAU Male 20 - 24 BAU
## 34 Omega3 Breast cancer BAU Male 20 - 24 BAU
## 35 Fish Depression BAU Male 20 - 24 BAU
## 36 Omega3 CHD2 mortality BAU Male 20 - 24 BAU
## 37 Fish All-cause mortality BAU Female 25 - 29 BAU
## 38 Omega3 Breast cancer BAU Female 25 - 29 BAU
## 39 Fish Depression BAU Female 25 - 29 BAU
## 40 Omega3 CHD2 mortality BAU Female 25 - 29 BAU
## 41 Fish All-cause mortality BAU Male 25 - 29 BAU
## 42 Omega3 Breast cancer BAU Male 25 - 29 BAU
## 43 Fish Depression BAU Male 25 - 29 BAU
## 44 Omega3 CHD2 mortality BAU Male 25 - 29 BAU
## 45 Fish All-cause mortality BAU Female 30 - 34 BAU
## 46 Omega3 Breast cancer BAU Female 30 - 34 BAU
## 47 Fish Depression BAU Female 30 - 34 BAU
## 48 Omega3 CHD2 mortality BAU Female 30 - 34 BAU
## 49 Fish All-cause mortality BAU Male 30 - 34 BAU
## 50 Omega3 Breast cancer BAU Male 30 - 34 BAU
## 51 Fish Depression BAU Male 30 - 34 BAU
## 52 Omega3 CHD2 mortality BAU Male 30 - 34 BAU
## 53 Fish All-cause mortality BAU Female 35 - 39 BAU
## 54 Omega3 Breast cancer BAU Female 35 - 39 BAU
## 55 Fish Depression BAU Female 35 - 39 BAU
## 56 Omega3 CHD2 mortality BAU Female 35 - 39 BAU
## 57 Fish All-cause mortality BAU Male 35 - 39 BAU
## 58 Omega3 Breast cancer BAU Male 35 - 39 BAU
## 59 Fish Depression BAU Male 35 - 39 BAU
## 60 Omega3 CHD2 mortality BAU Male 35 - 39 BAU
## 61 Fish All-cause mortality BAU Female 40 - 44 BAU
## 62 Omega3 Breast cancer BAU Female 40 - 44 BAU
## 63 Fish Depression BAU Female 40 - 44 BAU
## 64 Omega3 CHD2 mortality BAU Female 40 - 44 BAU
## 65 Fish All-cause mortality BAU Male 40 - 44 BAU
## 66 Omega3 Breast cancer BAU Male 40 - 44 BAU
## 67 Fish Depression BAU Male 40 - 44 BAU
## 68 Omega3 CHD2 mortality BAU Male 40 - 44 BAU
## 69 Fish All-cause mortality BAU Female 45 - 49 BAU
## 70 Omega3 Breast cancer BAU Female 45 - 49 BAU
## 71 Fish Depression BAU Female 45 - 49 BAU
## 72 Omega3 CHD2 mortality BAU Female 45 - 49 BAU
## 73 Fish All-cause mortality BAU Male 45 - 49 BAU
## 74 Omega3 Breast cancer BAU Male 45 - 49 BAU
## 75 Fish Depression BAU Male 45 - 49 BAU
## 76 Omega3 CHD2 mortality BAU Male 45 - 49 BAU
## 77 PFAS Immunosuppression BAU Female 5 - 9 BAU
## 78 Fish All-cause mortality BAU Female 5 - 9 BAU
## 79 Omega3 Breast cancer BAU Female 5 - 9 BAU
## 80 Fish Depression BAU Female 5 - 9 BAU
## 81 Omega3 CHD2 mortality BAU Female 5 - 9 BAU
## 82 PFAS Immunosuppression BAU Male 5 - 9 BAU
## 83 Fish All-cause mortality BAU Male 5 - 9 BAU
## 84 Omega3 Breast cancer BAU Male 5 - 9 BAU
## 85 Fish Depression BAU Male 5 - 9 BAU
## 86 Omega3 CHD2 mortality BAU Male 5 - 9 BAU
## 87 Fish All-cause mortality BAU Female 50 - 54 BAU
## 88 Omega3 Breast cancer BAU Female 50 - 54 BAU
## 89 Fish Depression BAU Female 50 - 54 BAU
## 90 Omega3 CHD2 mortality BAU Female 50 - 54 BAU
## 91 Fish All-cause mortality BAU Male 50 - 54 BAU
## 92 Omega3 Breast cancer BAU Male 50 - 54 BAU
## 93 Fish Depression BAU Male 50 - 54 BAU
## 94 Omega3 CHD2 mortality BAU Male 50 - 54 BAU
## 95 Fish All-cause mortality BAU Female 55 - 59 BAU
## 96 Omega3 Breast cancer BAU Female 55 - 59 BAU
## 97 Fish Depression BAU Female 55 - 59 BAU
## 98 Omega3 CHD2 mortality BAU Female 55 - 59 BAU
## 99 Fish All-cause mortality BAU Male 55 - 59 BAU
## 100 Omega3 Breast cancer BAU Male 55 - 59 BAU
## 101 Fish Depression BAU Male 55 - 59 BAU
## 102 Omega3 CHD2 mortality BAU Male 55 - 59 BAU
## 103 Fish All-cause mortality BAU Female 60 - 64 BAU
## 104 Omega3 Breast cancer BAU Female 60 - 64 BAU
## 105 Fish Depression BAU Female 60 - 64 BAU
## 106 Omega3 CHD2 mortality BAU Female 60 - 64 BAU
## 107 Fish All-cause mortality BAU Male 60 - 64 BAU
## 108 Omega3 Breast cancer BAU Male 60 - 64 BAU
## 109 Fish Depression BAU Male 60 - 64 BAU
## 110 Omega3 CHD2 mortality BAU Male 60 - 64 BAU
## 111 Fish All-cause mortality BAU Female 65 - 69 BAU
## 112 Omega3 Breast cancer BAU Female 65 - 69 BAU
## 113 Fish Depression BAU Female 65 - 69 BAU
## 114 Omega3 CHD2 mortality BAU Female 65 - 69 BAU
## 115 Fish All-cause mortality BAU Male 65 - 69 BAU
## 116 Omega3 Breast cancer BAU Male 65 - 69 BAU
## 117 Fish Depression BAU Male 65 - 69 BAU
## 118 Omega3 CHD2 mortality BAU Male 65 - 69 BAU
## 119 Fish All-cause mortality BAU Female 70 - 74 BAU
## 120 Omega3 Breast cancer BAU Female 70 - 74 BAU
## 121 Fish Depression BAU Female 70 - 74 BAU
## 122 Omega3 CHD2 mortality BAU Female 70 - 74 BAU
## 123 Fish All-cause mortality BAU Male 70 - 74 BAU
## 124 Omega3 Breast cancer BAU Male 70 - 74 BAU
## 125 Fish Depression BAU Male 70 - 74 BAU
## 126 Omega3 CHD2 mortality BAU Male 70 - 74 BAU
## 127 Fish All-cause mortality BAU Female 75 - 79 BAU
## 128 Omega3 Breast cancer BAU Female 75 - 79 BAU
## 129 Fish Depression BAU Female 75 - 79 BAU
## 130 Omega3 CHD2 mortality BAU Female 75 - 79 BAU
## 131 Fish All-cause mortality BAU Male 75 - 79 BAU
## 132 Omega3 Breast cancer BAU Male 75 - 79 BAU
## 133 Fish Depression BAU Male 75 - 79 BAU
## 134 Omega3 CHD2 mortality BAU Male 75 - 79 BAU
## 135 Fish All-cause mortality BAU Female 80 - 84 BAU
## 136 Omega3 Breast cancer BAU Female 80 - 84 BAU
## 137 Fish Depression BAU Female 80 - 84 BAU
## 138 Omega3 CHD2 mortality BAU Female 80 - 84 BAU
## 139 Fish All-cause mortality BAU Male 80 - 84 BAU
## 140 Omega3 Breast cancer BAU Male 80 - 84 BAU
## 141 Fish Depression BAU Male 80 - 84 BAU
## 142 Omega3 CHD2 mortality BAU Male 80 - 84 BAU
## 143 Fish All-cause mortality BAU Female 85+ BAU
## 144 Omega3 Breast cancer BAU Female 85+ BAU
## 145 Fish Depression BAU Female 85+ BAU
## 146 Omega3 CHD2 mortality BAU Female 85+ BAU
## 147 Fish All-cause mortality BAU Male 85+ BAU
## 148 Omega3 Breast cancer BAU Male 85+ BAU
## 149 Fish Depression BAU Male 85+ BAU
## 150 Omega3 CHD2 mortality BAU Male 85+ BAU
## 151 Vitamin D Vitamin D recommendation BAU Female Undefined BAU
## 152 PFAS PFAS TWI BAU Female Undefined BAU
## 153 Vitamin D Vitamin D recommendation BAU Male Undefined BAU
## 154 PFAS PFAS TWI BAU Male Undefined BAU
## mean
## 1 -0.15625100
## 2 0.01813625
## 3 -0.06412974
## 4 -0.19763757
## 5 -0.15262578
## 6 -0.15322440
## 7 -0.23437649
## 8 0.02720438
## 9 -0.09463548
## 10 -0.28128638
## 11 -0.21996719
## 12 -0.15843589
## 13 -0.06412974
## 14 -0.19763757
## 15 -0.15262578
## 16 -0.15322440
## 17 -0.09463548
## 18 -0.28128638
## 19 -0.21996719
## 20 -0.15843589
## 21 -0.06412974
## 22 -0.19763757
## 23 -0.15262578
## 24 -0.15322440
## 25 -0.09463548
## 26 -0.28128638
## 27 -0.21996719
## 28 -0.15843589
## 29 -0.06412974
## 30 -0.19763757
## 31 -0.15262578
## 32 -0.15322440
## 33 -0.09463548
## 34 -0.28128638
## 35 -0.21996719
## 36 -0.15843589
## 37 -0.06412974
## 38 -0.19763757
## 39 -0.15262578
## 40 -0.15322440
## 41 -0.09463548
## 42 -0.28128638
## 43 -0.21996719
## 44 -0.15843589
## 45 -0.06412974
## 46 -0.19763757
## 47 -0.15262578
## 48 -0.15322440
## 49 -0.09463548
## 50 -0.28128638
## 51 -0.21996719
## 52 -0.15843589
## 53 -0.06412974
## 54 -0.19763757
## 55 -0.15262578
## 56 -0.15322440
## 57 -0.09463548
## 58 -0.28128638
## 59 -0.21996719
## 60 -0.15843589
## 61 -0.06412974
## 62 -0.19763757
## 63 -0.15262578
## 64 -0.15322440
## 65 -0.09463548
## 66 -0.28128638
## 67 -0.21996719
## 68 -0.15843589
## 69 -0.06412974
## 70 -0.19763757
## 71 -0.15262578
## 72 -0.15322440
## 73 -0.09463548
## 74 -0.28128638
## 75 -0.21996719
## 76 -0.15843589
## 77 0.02208490
## 78 -0.06412974
## 79 -0.19763757
## 80 -0.15262578
## 81 -0.15322440
## 82 0.03312735
## 83 -0.09463548
## 84 -0.28128638
## 85 -0.21996719
## 86 -0.15843589
## 87 -0.06412974
## 88 -0.19763757
## 89 -0.15262578
## 90 -0.15322440
## 91 -0.09463548
## 92 -0.28128638
## 93 -0.21996719
## 94 -0.15843589
## 95 -0.06412974
## 96 -0.19763757
## 97 -0.15262578
## 98 -0.15322440
## 99 -0.09463548
## 100 -0.28128638
## 101 -0.21996719
## 102 -0.15843589
## 103 -0.06412974
## 104 -0.19763757
## 105 -0.15262578
## 106 -0.15322440
## 107 -0.09463548
## 108 -0.28128638
## 109 -0.21996719
## 110 -0.15843589
## 111 -0.06412974
## 112 -0.19763757
## 113 -0.15262578
## 114 -0.15322440
## 115 -0.09463548
## 116 -0.28128638
## 117 -0.21996719
## 118 -0.15843589
## 119 -0.06412974
## 120 -0.19763757
## 121 -0.15262578
## 122 -0.15322440
## 123 -0.09463548
## 124 -0.28128638
## 125 -0.21996719
## 126 -0.15843589
## 127 -0.06412974
## 128 -0.19763757
## 129 -0.15262578
## 130 -0.15322440
## 131 -0.09463548
## 132 -0.28128638
## 133 -0.21996719
## 134 -0.15843589
## 135 -0.06412974
## 136 -0.19763757
## 137 -0.15262578
## 138 -0.15322440
## 139 -0.09463548
## 140 -0.28128638
## 141 -0.21996719
## 142 -0.15843589
## 143 -0.06412974
## 144 -0.19763757
## 145 -0.15262578
## 146 -0.15322440
## 147 -0.09463548
## 148 -0.28128638
## 149 -0.21996719
## 150 -0.15843589
## 151 1.00000000
## 152 0.00000000
## 153 1.00000000
## 154 1.00000000oprint(summary(population,"mean"))## Gender Age mean
## 1 Female 0 - 4 125040
## 2 Male 0 - 4 130884
## 3 Female 10 - 14 151113
## 4 Male 10 - 14 157712
## 5 Female 15 - 19 144441
## 6 Male 15 - 19 152230
## 7 Female 20 - 24 152265
## 8 Male 20 - 24 161679
## 9 Female 25 - 29 172593
## 10 Male 25 - 29 183092
## 11 Female 30 - 34 169653
## 12 Male 30 - 34 181115
## 13 Female 35 - 39 174660
## 14 Male 35 - 39 186122
## 15 Female 40 - 44 168547
## 16 Male 40 - 44 177928
## 17 Female 45 - 49 154391
## 18 Male 45 - 49 159982
## 19 Female 5 - 9 149633
## 20 Male 5 - 9 156654
## 21 Female 50 - 54 176612
## 22 Male 50 - 54 179182
## 23 Female 55 - 59 185152
## 24 Male 55 - 59 183719
## 25 Female 60 - 64 183336
## 26 Male 60 - 64 176283
## 27 Female 65 - 69 185685
## 28 Male 65 - 69 171275
## 29 Female 70 - 74 186034
## 30 Male 70 - 74 163697
## 31 Female 75 - 79 118190
## 32 Male 75 - 79 93987
## 33 Female 80 - 84 96256
## 34 Male 80 - 84 65140
## 35 Female 85+ 103429
## 36 Male 85+ 47581
## 37 Female Undefined 2797030
## 38 Male Undefined 2728262oprint(summary(RR,"mean"))## Exposure_agent Response ER_function Scaling Scenario Gender
## 1 Fish All-cause mortality RR None BAU Female
## 2 Omega3 Breast cancer RR None BAU Female
## 3 Fish Depression RR None BAU Female
## 4 Omega3 CHD2 mortality Relative Hill None BAU Female
## 5 Fish All-cause mortality RR None BAU Male
## 6 Omega3 Breast cancer RR None BAU Male
## 7 Fish Depression RR None BAU Male
## 8 Omega3 CHD2 mortality Relative Hill None BAU Male
## 9 Fish All-cause mortality RR None BAU Female
## 10 Omega3 Breast cancer RR None BAU Female
## 11 Fish Depression RR None BAU Female
## 12 Omega3 CHD2 mortality Relative Hill None BAU Female
## 13 Fish All-cause mortality RR None BAU Male
## 14 Omega3 Breast cancer RR None BAU Male
## 15 Fish Depression RR None BAU Male
## 16 Omega3 CHD2 mortality Relative Hill None BAU Male
## 17 Fish All-cause mortality RR None BAU Female
## 18 Omega3 Breast cancer RR None BAU Female
## 19 Fish Depression RR None BAU Female
## 20 Omega3 CHD2 mortality Relative Hill None BAU Female
## 21 Fish All-cause mortality RR None BAU Male
## 22 Omega3 Breast cancer RR None BAU Male
## 23 Fish Depression RR None BAU Male
## 24 Omega3 CHD2 mortality Relative Hill None BAU Male
## 25 Fish All-cause mortality RR None BAU Female
## 26 Omega3 Breast cancer RR None BAU Female
## 27 Fish Depression RR None BAU Female
## 28 Omega3 CHD2 mortality Relative Hill None BAU Female
## 29 Fish All-cause mortality RR None BAU Male
## 30 Omega3 Breast cancer RR None BAU Male
## 31 Fish Depression RR None BAU Male
## 32 Omega3 CHD2 mortality Relative Hill None BAU Male
## 33 Fish All-cause mortality RR None BAU Female
## 34 Omega3 Breast cancer RR None BAU Female
## 35 Fish Depression RR None BAU Female
## 36 Omega3 CHD2 mortality Relative Hill None BAU Female
## 37 Fish All-cause mortality RR None BAU Male
## 38 Omega3 Breast cancer RR None BAU Male
## 39 Fish Depression RR None BAU Male
## 40 Omega3 CHD2 mortality Relative Hill None BAU Male
## 41 Fish All-cause mortality RR None BAU Female
## 42 Omega3 Breast cancer RR None BAU Female
## 43 Fish Depression RR None BAU Female
## 44 Omega3 CHD2 mortality Relative Hill None BAU Female
## 45 Fish All-cause mortality RR None BAU Male
## 46 Omega3 Breast cancer RR None BAU Male
## 47 Fish Depression RR None BAU Male
## 48 Omega3 CHD2 mortality Relative Hill None BAU Male
## 49 Fish All-cause mortality RR None BAU Female
## 50 Omega3 Breast cancer RR None BAU Female
## 51 Fish Depression RR None BAU Female
## 52 Omega3 CHD2 mortality Relative Hill None BAU Female
## 53 Fish All-cause mortality RR None BAU Male
## 54 Omega3 Breast cancer RR None BAU Male
## 55 Fish Depression RR None BAU Male
## 56 Omega3 CHD2 mortality Relative Hill None BAU Male
## 57 Fish All-cause mortality RR None BAU Female
## 58 Omega3 Breast cancer RR None BAU Female
## 59 Fish Depression RR None BAU Female
## 60 Omega3 CHD2 mortality Relative Hill None BAU Female
## 61 Fish All-cause mortality RR None BAU Male
## 62 Omega3 Breast cancer RR None BAU Male
## 63 Fish Depression RR None BAU Male
## 64 Omega3 CHD2 mortality Relative Hill None BAU Male
## 65 Fish All-cause mortality RR None BAU Female
## 66 Omega3 Breast cancer RR None BAU Female
## 67 Fish Depression RR None BAU Female
## 68 Omega3 CHD2 mortality Relative Hill None BAU Female
## 69 Fish All-cause mortality RR None BAU Male
## 70 Omega3 Breast cancer RR None BAU Male
## 71 Fish Depression RR None BAU Male
## 72 Omega3 CHD2 mortality Relative Hill None BAU Male
## 73 Fish All-cause mortality RR None BAU Female
## 74 Omega3 Breast cancer RR None BAU Female
## 75 Fish Depression RR None BAU Female
## 76 Omega3 CHD2 mortality Relative Hill None BAU Female
## 77 Fish All-cause mortality RR None BAU Male
## 78 Omega3 Breast cancer RR None BAU Male
## 79 Fish Depression RR None BAU Male
## 80 Omega3 CHD2 mortality Relative Hill None BAU Male
## 81 Fish All-cause mortality RR None BAU Female
## 82 Omega3 Breast cancer RR None BAU Female
## 83 Fish Depression RR None BAU Female
## 84 Omega3 CHD2 mortality Relative Hill None BAU Female
## 85 Fish All-cause mortality RR None BAU Male
## 86 Omega3 Breast cancer RR None BAU Male
## 87 Fish Depression RR None BAU Male
## 88 Omega3 CHD2 mortality Relative Hill None BAU Male
## 89 Fish All-cause mortality RR None BAU Female
## 90 Omega3 Breast cancer RR None BAU Female
## 91 Fish Depression RR None BAU Female
## 92 Omega3 CHD2 mortality Relative Hill None BAU Female
## 93 Fish All-cause mortality RR None BAU Male
## 94 Omega3 Breast cancer RR None BAU Male
## 95 Fish Depression RR None BAU Male
## 96 Omega3 CHD2 mortality Relative Hill None BAU Male
## 97 Fish All-cause mortality RR None BAU Female
## 98 Omega3 Breast cancer RR None BAU Female
## 99 Fish Depression RR None BAU Female
## 100 Omega3 CHD2 mortality Relative Hill None BAU Female
## 101 Fish All-cause mortality RR None BAU Male
## 102 Omega3 Breast cancer RR None BAU Male
## 103 Fish Depression RR None BAU Male
## 104 Omega3 CHD2 mortality Relative Hill None BAU Male
## 105 Fish All-cause mortality RR None BAU Female
## 106 Omega3 Breast cancer RR None BAU Female
## 107 Fish Depression RR None BAU Female
## 108 Omega3 CHD2 mortality Relative Hill None BAU Female
## 109 Fish All-cause mortality RR None BAU Male
## 110 Omega3 Breast cancer RR None BAU Male
## 111 Fish Depression RR None BAU Male
## 112 Omega3 CHD2 mortality Relative Hill None BAU Male
## 113 Fish All-cause mortality RR None BAU Female
## 114 Omega3 Breast cancer RR None BAU Female
## 115 Fish Depression RR None BAU Female
## 116 Omega3 CHD2 mortality Relative Hill None BAU Female
## 117 Fish All-cause mortality RR None BAU Male
## 118 Omega3 Breast cancer RR None BAU Male
## 119 Fish Depression RR None BAU Male
## 120 Omega3 CHD2 mortality Relative Hill None BAU Male
## 121 Fish All-cause mortality RR None BAU Female
## 122 Omega3 Breast cancer RR None BAU Female
## 123 Fish Depression RR None BAU Female
## 124 Omega3 CHD2 mortality Relative Hill None BAU Female
## 125 Fish All-cause mortality RR None BAU Male
## 126 Omega3 Breast cancer RR None BAU Male
## 127 Fish Depression RR None BAU Male
## 128 Omega3 CHD2 mortality Relative Hill None BAU Male
## 129 Fish All-cause mortality RR None BAU Female
## 130 Omega3 Breast cancer RR None BAU Female
## 131 Fish Depression RR None BAU Female
## 132 Omega3 CHD2 mortality Relative Hill None BAU Female
## 133 Fish All-cause mortality RR None BAU Male
## 134 Omega3 Breast cancer RR None BAU Male
## 135 Fish Depression RR None BAU Male
## 136 Omega3 CHD2 mortality Relative Hill None BAU Male
## 137 Fish All-cause mortality RR None BAU Female
## 138 Omega3 Breast cancer RR None BAU Female
## 139 Fish Depression RR None BAU Female
## 140 Omega3 CHD2 mortality Relative Hill None BAU Female
## 141 Fish All-cause mortality RR None BAU Male
## 142 Omega3 Breast cancer RR None BAU Male
## 143 Fish Depression RR None BAU Male
## 144 Omega3 CHD2 mortality Relative Hill None BAU Male
## Age mean
## 1 0 - 4 0.9358703
## 2 0 - 4 0.8023624
## 3 0 - 4 0.8473742
## 4 0 - 4 0.8467756
## 5 0 - 4 0.9053645
## 6 0 - 4 0.7187136
## 7 0 - 4 0.7800328
## 8 0 - 4 0.8415641
## 9 10 - 14 0.9358703
## 10 10 - 14 0.8023624
## 11 10 - 14 0.8473742
## 12 10 - 14 0.8467756
## 13 10 - 14 0.9053645
## 14 10 - 14 0.7187136
## 15 10 - 14 0.7800328
## 16 10 - 14 0.8415641
## 17 15 - 19 0.9358703
## 18 15 - 19 0.8023624
## 19 15 - 19 0.8473742
## 20 15 - 19 0.8467756
## 21 15 - 19 0.9053645
## 22 15 - 19 0.7187136
## 23 15 - 19 0.7800328
## 24 15 - 19 0.8415641
## 25 20 - 24 0.9358703
## 26 20 - 24 0.8023624
## 27 20 - 24 0.8473742
## 28 20 - 24 0.8467756
## 29 20 - 24 0.9053645
## 30 20 - 24 0.7187136
## 31 20 - 24 0.7800328
## 32 20 - 24 0.8415641
## 33 25 - 29 0.9358703
## 34 25 - 29 0.8023624
## 35 25 - 29 0.8473742
## 36 25 - 29 0.8467756
## 37 25 - 29 0.9053645
## 38 25 - 29 0.7187136
## 39 25 - 29 0.7800328
## 40 25 - 29 0.8415641
## 41 30 - 34 0.9358703
## 42 30 - 34 0.8023624
## 43 30 - 34 0.8473742
## 44 30 - 34 0.8467756
## 45 30 - 34 0.9053645
## 46 30 - 34 0.7187136
## 47 30 - 34 0.7800328
## 48 30 - 34 0.8415641
## 49 35 - 39 0.9358703
## 50 35 - 39 0.8023624
## 51 35 - 39 0.8473742
## 52 35 - 39 0.8467756
## 53 35 - 39 0.9053645
## 54 35 - 39 0.7187136
## 55 35 - 39 0.7800328
## 56 35 - 39 0.8415641
## 57 40 - 44 0.9358703
## 58 40 - 44 0.8023624
## 59 40 - 44 0.8473742
## 60 40 - 44 0.8467756
## 61 40 - 44 0.9053645
## 62 40 - 44 0.7187136
## 63 40 - 44 0.7800328
## 64 40 - 44 0.8415641
## 65 45 - 49 0.9358703
## 66 45 - 49 0.8023624
## 67 45 - 49 0.8473742
## 68 45 - 49 0.8467756
## 69 45 - 49 0.9053645
## 70 45 - 49 0.7187136
## 71 45 - 49 0.7800328
## 72 45 - 49 0.8415641
## 73 5 - 9 0.9358703
## 74 5 - 9 0.8023624
## 75 5 - 9 0.8473742
## 76 5 - 9 0.8467756
## 77 5 - 9 0.9053645
## 78 5 - 9 0.7187136
## 79 5 - 9 0.7800328
## 80 5 - 9 0.8415641
## 81 50 - 54 0.9358703
## 82 50 - 54 0.8023624
## 83 50 - 54 0.8473742
## 84 50 - 54 0.8467756
## 85 50 - 54 0.9053645
## 86 50 - 54 0.7187136
## 87 50 - 54 0.7800328
## 88 50 - 54 0.8415641
## 89 55 - 59 0.9358703
## 90 55 - 59 0.8023624
## 91 55 - 59 0.8473742
## 92 55 - 59 0.8467756
## 93 55 - 59 0.9053645
## 94 55 - 59 0.7187136
## 95 55 - 59 0.7800328
## 96 55 - 59 0.8415641
## 97 60 - 64 0.9358703
## 98 60 - 64 0.8023624
## 99 60 - 64 0.8473742
## 100 60 - 64 0.8467756
## 101 60 - 64 0.9053645
## 102 60 - 64 0.7187136
## 103 60 - 64 0.7800328
## 104 60 - 64 0.8415641
## 105 65 - 69 0.9358703
## 106 65 - 69 0.8023624
## 107 65 - 69 0.8473742
## 108 65 - 69 0.8467756
## 109 65 - 69 0.9053645
## 110 65 - 69 0.7187136
## 111 65 - 69 0.7800328
## 112 65 - 69 0.8415641
## 113 70 - 74 0.9358703
## 114 70 - 74 0.8023624
## 115 70 - 74 0.8473742
## 116 70 - 74 0.8467756
## 117 70 - 74 0.9053645
## 118 70 - 74 0.7187136
## 119 70 - 74 0.7800328
## 120 70 - 74 0.8415641
## 121 75 - 79 0.9358703
## 122 75 - 79 0.8023624
## 123 75 - 79 0.8473742
## 124 75 - 79 0.8467756
## 125 75 - 79 0.9053645
## 126 75 - 79 0.7187136
## 127 75 - 79 0.7800328
## 128 75 - 79 0.8415641
## 129 80 - 84 0.9358703
## 130 80 - 84 0.8023624
## 131 80 - 84 0.8473742
## 132 80 - 84 0.8467756
## 133 80 - 84 0.9053645
## 134 80 - 84 0.7187136
## 135 80 - 84 0.7800328
## 136 80 - 84 0.8415641
## 137 85+ 0.9358703
## 138 85+ 0.8023624
## 139 85+ 0.8473742
## 140 85+ 0.8467756
## 141 85+ 0.9053645
## 142 85+ 0.7187136
## 143 85+ 0.7800328
## 144 85+ 0.8415641###################
# Graphs
trim <- function(ova) return(oapply(ova, NULL, mean, "Iter")@output)
ggplot(amount@output, aes(x=Gender, weight=amountResult, fill=Kala))+geom_bar()+
labs(
title="Kalansyönti Suomessa ikäryhmittäin",
y="Syönti (g/d)"
)
ggsave("Kalansyönti Suomessa ikäryhmittäin.svg")## Saving 7 x 5 in imageplot_ly(trim(total_amount), x=~Kala, y=~total_amountResult, color=~Kala, type="bar") %>%
layout(yaxis=list(title="Kalan kokonaiskulutus Suomessa (milj kg /a)"), barmode="stack")## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.plot_ly(trim(conc_vit), x=~Nutrient, y=~conc_vitResult, color=~Kala, type="scatter", mode="markers") %>%
layout(yaxis=list(title="Concentrations of nutrients (mg or ug /g)"))## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsggplot(conc_pfas@output, aes(x=Fish, y=conc_pfasResult))+geom_point() # Inputed data for missing species.
ggplot(conc_pfas@output, aes(x=conc_pfasResult, color=Compound, linetype=Area))+stat_ecdf()+
scale_x_log10()+
stat_ecdf(data=conc_eukalat@output, aes(x=conc_eukalatResult))+
scale_linetype_manual(values=c("dotted","solid","twodash"))+
labs(
title="PFAS concentration in fishes in Finland",
x="PFAS concentration (ng/g fresh weight)",
y="Cumulative probability"
)
# The code may produce some negative values, which are removed from the graph
ggsave("PFAS-pitoisuus kalassa Suomessa.svg")## Saving 7 x 5 in imageggplot(conc@output, aes(x=concResult, colour=Fish))+stat_ecdf()+
facet_wrap(~Compound, scales="free_x")
#ggplot(oapply(expo_dir, cols=c("Iter"),FUN=mean)@output,
# aes(x=Gender, weight=expo_dirResult,fill=Fish))+geom_bar()+
# facet_wrap(~Compound, scales="free_y")+
# labs(title="Eri yhdisteiden saanti kalasta")
#ggsave("Yhdisteiden saanti kalasta Suomessa.svg")
tmp <- exposure #/ Ovariable(
# output = data.frame(
# Exposure_agent = c("Fish","Vitamin D", "Omega3", "ALA", "DHA", "TEQ", "PFAS"),
# Result = c(1, 1, 1000, 1000, 1000, 1, 1)
# ),
# marginal = c(TRUE, FALSE)
#)
plot_ly(trim(tmp), x=~exposureSource, y=~exposureResult, color=~Exposure_agent, text=~Exposure_agent, type="bar") %>%
layout(yaxis=list(title="Exposure to nutrients (g or ug /d)"))ggplot(exposure@output, aes(x=Gender, weight=exposureResult, fill=Gender))+geom_bar()+
facet_wrap(~Exposure_agent, scales="free_y")+
labs(
title="Exposure to compounds",
y="(omega: mg/d; vit D: ug/d, PFAS: ng/d)"
)
cat("Kalaperäisiä tautitaakkoja Suomessa\n")## Kalaperäisiä tautitaakkoja Suomessaif(openv$N>1) {
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Response")))
tmp <- data.frame(
Altiste = tmp$Exposure_agent,
Vaikutus = tmp$Response,
Keskiarvo = as.character(signif(tmp$mean,2)),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)#[rev(match(lev, tmp$Exposure_agent)),]
oprint(tmp)
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent")))
tmp <- data.frame(
Terveysvaikutus = tmp$Response,
Keskiarvo = signif(tmp$mean,2),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)
oprint(tmp)
}
ggplot(trim(BoDattr), aes(x=Exposure_agent, weight=BoDattrResult, fill=Response))+geom_bar()
ggplot(trim(BoDattr), aes(x=Response, weight=BoDattrResult, fill=Exposure_agent))+geom_bar()
plot_ly(trim(BoDattr), x=~Exposure_agent, y=~BoDattrResult, color=~Response, text=~paste(Age, Exposure_agent, sep=": "), type="bar") %>%
layout(yaxis=list(title="Disease burden (DALY /a); CHD2=coronary heart disease"), barmode="stack")## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors################ Insight network
gr <- scrape(type="assessment")
objects.latest("Op_en3861", "makeGraph") # [[Insight network]]## Loading objects:
## makeGraphgr <- makeGraph(gr)## Loading required package: DiagrammeR## Loading objects:
## formatted
## Loading objects:
## chooseGr#export_graph(gr, "ruori.svg")
#render_graph(gr) # Does not work: Error in generate_dot(graph) : object 'attribute' not found
##################### Diagnostics
objects.latest("Op_en6007", code_name="diagnostics")## Loading objects:
## showind
## binoptest
## showLoctable
## ovashapetestshowLoctable()showind()## subgrouping is not an ovariable.
## sumExposcen is not an ovariable.
## mc2d is not an ovariable.
## mc2dparam is not an ovariable.